Abstract

Due to their potential applications in various situations such as battlefield communications, emergency relief, environmental monitoring, and other special-purpose operations, wireless sensor networks have recently emerged as a new and exciting research area that has attracted a good deal of well-deserved attention in the literature. In this work we take the view that a sensor network consists of a set of tiny sensors, massively deployed over a geographical area. The sensors are capable of performing processing, sensing and communicating with each other by radio links. Alongside, with the tiny sensors, more powerful devices referred as Aggregating and Forwarding Nodes, (AFN, for short) are also deployed. In support of their mission, the AFNs are endowed with a special radio interface for long distance communications, miniaturized GPS, and appropriate networking tools for data collection and aggregation. As a fundamental prerequisite for self-organization, the sensors need to acquire some form of location awareness. Since fine-grain location awareness usually assumes that the sensors are GPS-enabled, in the case of tiny sensors the best we can hope for is to endow them with coarse-grain location awareness. This task is referred to as training and its responsibility lies with the AFNs. However, due to the random deployment, some of the sensors fall under the coverage area of several AFNs, in which case the goal is for these sensors to acquire location information relative to all the covering AFNs. The corresponding task is referred to as multi-training.The main contribution of this work is to show that in case the conflict graphs of the AFN coverage is bipartite, multi-training can be completed very fast by a simple algorithm.